在十亿个向量中搜索：用信源编码重新排序
Searching in one billion vectors: re-rank with source coding
摘要：
受信源编码启发的最新索引技术已经成功地索引了存储器中的数十亿个高维向量。在本文中，我们提出了一种方法，这些压缩域索引方法获得的邻居假设重新排名。与通常的验证后方案相比，该方案在短假设列表上执行精确的距离计算，基于短量化码来细化估计的距离，以避免从磁盘读取完整的向量。我们已经发布了一个新的10亿128维向量的公共数据集，并提出了一个实验设置，以评估现实规模的高维索引算法。实验表明，我们的方法准确，有效地重新排名的邻居假设使用较少的内存相比，全矢量表示。
英文摘要：
Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation on the short-list of hypotheses, the estimated distances are refined based on short quantization codes, to avoid reading the full vectors from disk. We have released a new public dataset of one billion 128-dimensional vectors and proposed an experimental setup to evaluate high dimensional indexing algorithms on a realistic scale. Experiments show that our method accurately and efficiently re-ranks the neighbor hypotheses using little memory compared to the full vectors representation.
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